Evaluating Neural Morphological Taggers for Sanskrit

Abstract

Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that explicitly model the internal structure of a label are more suited for the task, because of their ability to generalise to labels not seen during training. We find that although some neural models perform better than others, one of the common causes for error for all of these models is mispredictions due to syncretism. 1

Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit

“…1,500 and 1,000 sentences from STBC, other than the ones in test data, were used as the training and validation data respectively for DCST, DCST++, and BiAFF. However all the EBM models and YAP were trained on 12,320 sentences obtained by augmenting the training data in STBC (Krishna et al, 2020 (Krishna et al, 2020), they perform similar, with a small improvement of 0.77 points for MG-EBM .…”

“…For the joint morphosyntactic setting, we propose DCST++ as a neural baseline. DCST++ is our augmentation over DCST which integrates encoder outputs from a neural morphological tagger (Gupta et al, 2020) by a gating mechanism (Sato et al, 2017). 5 Metric: All the results we report are macro averaged at a sentence level.…”

“…In MG-EBM, we use this information to prune the input search space. Krishna et al (2020) proposed an energy basedmodel (EBM) framework for multiple structured prediction tasks in Sanskrit. For all the models under EBM, the input search space is a graph which considers every unique morphological analysis of the input words to be a separate node (Figure 1a).…”

“…Our proposed pruning approach in itself report an improvement of 6 F-score (2 UAS) points with the original EBM configuration for MP (DP), and a further 2 F-Score (2 UAS) points improvement with the multigraph formulation for MP (DP). Krishna et al (2020) proposed an Energy based model (EBM) framework (LeCun et al, 2006) for multiple structured prediction tasks in Sanskrit. The framework is a generalisation of the joint word-segmentation (WS) and morphological parsing (MP) model by Krishna et al (2018).…”

“…We propose a graph-based model for joint morphological parsing and dependency parsing in Sanskrit. Here, we extend the Energy based model framework (Krishna et al, 2020), proposed for several structured prediction tasks in Sanskrit, in 2 simple yet significant ways. First, the framework's default input graph generation method is modified to generate a multigraph, which enables the use of an exact search inference.…”